Glioma is a lethal disease with multiple genetic and epigenetic alterations. These changes work in concert in a coordinated fashion in cancer development and progression. Cancer Systems Biology is an emerging discipline in which high-throughput genomic data and computational approaches are integrated to provide a coherent and systematic understanding of the diverse pathway dysregulations responsible for the presentation of the same cancer phenotype. This new discipline promises to transform the practice of medicine from a reactive one to a predictive one. Here we will apply a reverse engineering computational approach to dissect and validate the transcriptional network that drives the mesenchymal phenotype of high-grade glioma. The expression of mesenchymal and angiogenesis-associated genes in malignant human glioma is associated with very poor clinical outcome. We have used ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks), one of the tools developed by the Columbia National Center for Biomedical Computing (MAGNet), to identify transcription factors regulating a mesenchymal gene expression signature associated with poor prognosis. The latter was identified by hierarchical clustering of a wide collection of microarray expression profiles of malignant glioma. Our analysis has identified a highly interconnected module of six transcription factors that regulate each other as well as the vast majority of the mesenchymal genes. We have also extended the computational analyses to new algorithms able to predict post-translational modulators of the master transcriptional regulators (MINDy, Modulator Inference by Network Dynamics). In this proposal we will design and use new computational tools to integrate the many sources of genetic, epigenetic and functional date available on human brain tumors. Our goals are: to reconstruct and experimentally manipulate the transcriptional and post-translational programs responsible for the expression of the mesenchymal signature of high-grade glioma (Aim 1); to elucidate the mechanism by which high-grade glioma silence ZNF238, a transcriptional repressor of the mesenchymal signature, and test the role of ZNF238 gene inactivation in gliomagenesis in the mouse (Aim 2); to computationally identify and experimentally validate druggable genes that regulate the mesenchymal signature in malignant glioma and to test them as candidate therapeutic targets (Aim 3); to assemble and disseminate a genome-wide, Human Glioma interactome (HGi) that will integrate the diverse sources of genetic, epigenetic, and functional alterations that characterize the mesenchymal phenotype of high-grade glioma (Aim 4). The HGi will be accessible to the scientific community via the MAGNet Center dissemination infrastructure. Ultimately, we aim to exploit the computationally inferred and experimentally validated regulators of glioma aggressiveness as invaluable new targets for therapeutic intervention. Public Health Relevance: High-grade gliomas (also known as glioblastoma mutiforme) are the fastest growing type of central nervous system tumors in humans. They are invariably associated with a bad prognosis. Recently, two intrinsic capabilities of glioblastoma multiforme have been recognized as key traits of malignancy, the ability to invade adjacent normal brain and produce new blood vessels. There is general agreement that the current failures in the treatment of high-grade gliomas are mostly caused by lack of effective tools against these features of aggressive brain tumors. This grant proposal uses cutting-edge computational technologies recently developed at Columbia University Medical Center to exploit microarray expression profiles from a large collection of primary glioblastoma multiforme and identify the key genetic master regulators responsible for the most aggressive attributes of malignant gliomas (the mesenchymal signature). We will perform a series of state-of-the-art experiments in cell culture systems and experimental animals to validate the predictions made by the new algorithms. We anticipate that the identification of the master genetic programs directing the most aggressive features of malignant gliomas will offer the best possible targets for therapeutic intervention.